Fidelity Heritage Union analytics dashboard displayed on a workstation used for capital allocation modelling
Predictive capital allocation

Stabilising variable freelance income through modelled capital allocation

Fidelity Heritage Union analyses your income patterns and market data in real time, then proposes measured allocation strategies for surplus funds between contracts. Every recommendation is accompanied by a daily report showing exactly how it performed.

The problem with irregular income

Freelance earnings arrive unevenly, which complicates saving and investing decisions

A quiet month followed by three invoices in one week is a familiar pattern for independent workers. Without a structured way to handle surplus cash, freelancers tend to either leave it idle or make ad-hoc decisions with limited visibility into risk.

Fidelity Heritage Union was built to sit between those two extremes. It ingests your income timing and relevant market data, builds a predictive model of near-term volatility, and proposes allocation strategies sized to what you can reasonably commit between projects.

  • Allocation recommendations sized to your actual cash-flow rhythm, not a fixed monthly figure.
  • Risk mitigation built into every proposal, based on modelled downside scenarios.
  • A daily report replacing guesswork with a documented, checkable record.
  • No requirement to interpret raw data or understand the underlying models yourself.
Fidelity Heritage Union analyst reviewing predictive allocation models on screen
Core capabilities

How the decision-optimisation engine works, in practical terms

Each function below feeds into the same daily report, so the reasoning behind a recommendation is always visible.

Data ingestion

Real-time data ingestion

Income timing, transaction history, and relevant market indicators are pulled in continuously, so recommendations reflect current conditions rather than a static snapshot from onboarding.

Risk modelling

Predictive risk modelling

The engine estimates the likelihood and scale of downside scenarios before any allocation is proposed, so risk is quantified up front rather than discovered afterwards.

Allocation logic

Automated allocation strategies

Surplus capital is distributed according to rules calibrated to your risk tolerance and cash-flow pattern, with adjustments applied automatically as new data arrives.

Reporting

Daily performance reporting

A concise report is generated each day, showing what changed, why, and how the current allocation is performing against its modelled expectations.

Methodology

How data becomes a recommendation you can check

Transparency is built into the process, not added afterwards as a summary slide.

01

Data sourcing

Inputs are drawn from your linked income records and established market data feeds. No data source is used without being identified in your account settings, and you can review what has been ingested at any time.

02

Model validation

Predictive models are tested against historical data before being applied to live recommendations, and their assumptions are re-checked on a rolling basis as new outcomes accumulate. This does not eliminate uncertainty, but it keeps the model's expected error rate visible.

03

Actionable insight generation

Outputs are translated from statistical terms into a short list of allocation actions, each with a stated rationale, so you can accept, adjust, or decline a recommendation with a clear understanding of what it is based on.

Transparency, not testimonials

What the daily report actually shows

Rather than relying on quoted opinions, Fidelity Heritage Union gives you direct access to the metrics behind every recommendation.

Sample daily report — summary view

Illustrative layout
Capital currently allocatedUnder model control
Modelled risk bandModerate
Recommendation issued todayRebalance proposed
Deviation from forecastWithin expected range
Next scheduled reviewTomorrow, 06:00

Metric breakdown

Each report separates three things: the data used, the model's confidence in its own forecast, and the specific action recommended. Confidence is expressed as a range rather than a single figure, because a model that overstates its certainty is more dangerous than one that is honest about its limits.

Our transparency commitment: no recommendation is issued without an accompanying explanation, and no report figure is adjusted retroactively without a visible correction note.

Frequently asked questions

Questions specific to UK freelancers and AI-driven allocation

How is my financial data secured?

Account and transaction data are encrypted in transit and at rest, and access to raw data is restricted to the automated systems that require it for modelling. You can review connected data sources and revoke access from your account settings at any time.

Can I access my allocated capital when I need it?

Allocation strategies are designed around liquidity bands you set in advance, reflecting how quickly you may need to draw on funds between contracts. The daily report always states how much of your allocated capital sits in an accessible position versus a longer-horizon one.

How accurate are the predictive models, realistically?

No predictive model removes market or income uncertainty. What the model provides is a consistent, tested framework for weighing likely outcomes, along with a stated confidence range on each forecast. Accuracy is reviewed on a rolling basis and reported honestly, including when forecasts miss.

Start with a demonstration, not a commitment

A platform walkthrough shows how data ingestion, risk modelling, and daily reporting work together using representative scenarios relevant to freelance income.

Request a platform demonstration

No obligation to open an account. You can ask for the demonstration alone and decide afterwards.